This invention relates to seismic exploration and processing, and more specifically to imaging with beams and a method to predict multiples based on primaries by combining both model-driven and data-driven methodologies.
In the petroleum industry, seismic prospecting techniques are commonly used to aid in the search for and the evaluation of subterranean hydrocarbon deposits. In seismic prospecting, one or more sources of seismic energy emit waves into a subsurface region of interest such as a geologic formation. These waves enter the formation and may be scattered, e.g., by reflection or refraction, by subsurface seismic reflectors (i.e., interfaces between underground formations having different elastic properties). The reflected signals are sampled or measured by one or more receivers, and the resultant data are recorded. The recorded samples may be referred to as seismic data or a set of “seismic traces”. The seismic data may be analyzed to extract details of the structure and properties of the region of the earth being explored.
Seismic prospecting consists of three separate stages: data acquisition, data processing and data interpretation. The success of a seismic prospecting operation depends on satisfactory completion of all three stages.
In general, the purpose of seismic exploration is to map or image a portion of the subsurface of the earth (a formation) by transmitting energy down into the ground and recording the “reflections” or “echoes” that return from the rock layers below. The energy transmitted into due formation is typically sound and shear wave energy. The downward-propagating sound energy may originate from various sources, such as explosions or seismic vibrators on land or air guns in marine environments. Seismic exploration typically uses one or more energy sources and typically a large number of sensors or detectors. The sensors that may be used to detect the returning seismic energy are usually geophones (land surveys) or hydrophones (marine surveys).
During a surface seismic survey, the energy source may be positioned at one or more locations near the surface of the earth above a geologic structure or formation of interest, referred to as shotpoints. Each time the source is activated, the source generates a seismic signal that travels downward through the earth and is at least partially reflected from discontinuities of various types in the subsurface, including reflections from “rock layer” boundaries. In general, a partial reflection of seismic signals may occur each time there is a change in the elastic properties of the subsurface materials. Reflected seismic signals are transmitted back to the surface of the earth, where they are recorded as a function of traveltime at a number of locations. The returning signals are digitized and recorded as a function of time (amplitude vs. time).
One prevalent issue with the seismic energy recorded by the receivers during the data acquisition stage is that the seismic traces often contain both the desired seismic reflections (the “primary” reflections) and unwanted multiple reflections which can obscure or overwhelm the primary seismic reflections. A primary reflection is a sound wave that passes from the source to a receiver with a single reflection from a subsurface seismic reflector. A multiple reflection is a wave that has reflected at least three times (up, down and back up again) before being detected by a receiver. Depending on their time delay from primary events with which they, are associated, multiples are commonly characterized as short-path, implying that they interfere with their own primary reflection, or long-path, where they appear as separate events.
There are also a variety of multiple events which are well known in the art. There are signals which are “trapped” in the water layer between two strong reflectors, the free surface and the bottom of the water layer. There are “peg-leg” multiple events, which are reflections that are characterized by an additional roundtrip through the water layer just after emission or just before detection. There are “remaining” surface-related multiple events, where the first and last upward reflections are below the first (water) layer, and there is at least one reflection at the free surface in between. There are also “interbed” multiples which has a downward reflection occurring from a subsurface reflector.
In most cases, multiples do not contain any useful information that is not more easily extracted from primaries. Moreover, water-bottom multiples have been recognized as the most serious noise problem in seismic data processing in many offshore areas. Multiples can severely mask primary reflection events for structural imaging and contaminate Amplitude vs. Offset (“AVO”) information. For those reasons, removal of multiples, or at least attenuation of multiples is a necessary part of the seismic data processing stage in many environments, particularly in marine settings where multiples are especially strong relative to the primaries.
In the case of deep-water data, suppression of first-order and the next few orders of sea-bottom multiple and peg-leg reflections are of great importance. These rather strong multiples may have the same travel time as the primary reflections of target reflectors.
There are several prior art methods to attenuate multiples depending on the attributes of the multiples utilized. One class of multiple attenuation methods is the predictive methods where the multiples are predicted from their respective primaries. Prior art predictive multiple attenuation techniques can be generally divided into two categories; model-driven methodologies and data-driven methodologies. Model-driven methodologies generally use an earth model and the recorded data to predict or simulate multiples utilizing the estimated sea-bottom reflectivity function and calculated amplitude functions to model water-layer multiple reflections, those predicted multiples are then subtracted from: the original data. Other model-driven technologies utilize an earth model or reflectivity model to predict the stationary multiples. The data-driven methodologies exploit the fact that primaries and multiples are physically related through a convolutional relationship and predict multiples by crossconvolving the relevant primaries thought to contain the stationary contributions for multiples. Data-driven methodologies can generally handle complex geometries and need little or no information about the properties of the subsurface. The model-based technologies are typically cost-effective compared to data-driven technologies, while the latter are typically more flexible.
Some model-driven methodologies require structural information, i.e., information about the subsurface structure, the determination of which is the reason for doing seismic exploration in the first place. Other model-driven methodologies require the shape of the source wavelet that will not be a pure delta function because of the reverberations and frequency bandwidth limitation. Some model-driven methodologies require both structural and source wavelet information while others use a matching filter to account for a distorted source wavelet.
Data-driven methodologies rely on the predictability of multiples from primary components. In effect, that methodology utilizes existing seismic data to generate multiples and those generated multiples are then subtracted from the existing data. One such prior art methodology that is data-driven is known as “surface-related multiple elimination” or “SRME”. In brief, this method operates by utilizing the existing data to create a dataset that contains only predictions of the multiples that are present in the data. Specifically, the method seeks to predict the seismic expression of multiples, and after adaptation to the existing multiples in the data the predicted multiples are subtracted from the original data leaving behind (at least theoretically) only the primary energy.
Data-driven SRME techniques are attractive solutions for predicting multiples in complex geologic settings, they do no require any a-priori knowledge of the subsurface (reflectivity, structures and velocities). However, these methods do require one shot location for each receiver position, and this is not the case for most three dimensional (“3D”) acquisition geometries. SRME methodologies are generally challenged by complex 3D multiples because of large shot spacing, narrow spread length and/or wide cable spacing. The missing data can be interpolated or extrapolated from the existing data, but interpolation of extrapolation has trouble with aliased seismic data caused by the large shot and/or receiver spacing. Advanced interpolation or extrapolation methods can also be difficult to implement and expensive. A common cause of these complex 3D data that challenge 3D SRME methods is rugosity on the top of salt. But, any type of complex overburden can cause complex 3D seismic data that is hard to interpolate.
Another data-driven methodology utilizes predictive deconvolution which is a filtering method that assumes that multiples are periodic while primaries are not. This assumption is usually met for data from water depths less than 500 msec (approximately 1,200 feet) and approximately layered subsurface geology. In areas of water depths greater than 500 msec where the velocity difference between primaries and multiples are significant, velocity-filtering methods (as opposed to predictive methods) such as tau-p and f-k filtering can be used, where the variable f represents frequency, k represents the wave-number, p represents the ray parameter, and tau represents the zero offset intercept time.
However, filtering methods generally require determination, or at least an educated guess, of the apparent wave propagation velocities in the subsurface media through which the reflected seismic waves pass in their journey from the seismic source to a receiver. These velocities can differ significantly due to the combination of the variations of the subsurface structure and rock properties. In addition, predictive deconvolution often leads to inadvertent damage to the primaries due to the difficulty in separating the multiples and primaries. Moreover, predictive deconvolution often fails to take into account the nonlinear factor in the reflectivity, which are generally caused by peg-leg multiples.
One prior art method which has extended predictive deconvolution for applications in deep water has utilized beam techniques. That method applies local slant stacking (or other dip-discriminating methods) to the data to decompose the recorded wavefields into beam components. These components travel approximately along raypaths. Simple raytracing within the water layer describes the long-period reverberations and relate primary and multiple events occurring in the beam components of the wave-field. Based on the information from the raytracing the time series of the beam-component of the primary can be shifted according to raytraced traveltimes and then analyzed with a multi-channel prediction filter. The predicted time series is considered as multiple energies and is removed from the beam components of the original data after a multi-channel matched filtering.
FIG. 1 illustrates a flowchart for one example of a prior art method 2 wherein deconvolution is utilized with a beam technique for attenuating multiples. The prior art method includes initializing an earth model 4 which relates to a geological region of interest, and initiating a beam dataset 6 that has been determined from seismic data of the geological region of interest. The prior art method further includes a series of loops wherein an input beam 3, a multiple-generating surface 10 and a time gate 12 are selected. One or more time gates (or windows) are selected to ensure that the signal within each gate is stationary. Trial rays are then “sprayed” from a detector location 14 and a stationary pegleg is determined 16. The stationary pegleg is the pegleg that satisfies the Snell's law for reflection at the multiple-generating surface. A primary beam corresponding to the stationary pegleg is obtained 13, and the primary beam is transformed into a predicted multiples beam 20. The predicted multiples beam is then deconvolved to remove multiples which are present in the input beam 22. Deconvolving the predicted multiples beam to remove the multiples in the input beam 22 can occur within the loop for selecting the time gate 24, the loop for selecting the surface 26, or the loop for selecting the input beam 28.
While the beam techniques have improved prior art multiple attenuation techniques, there is still a need for an improved method which provides a more accurate prediction of multiples and therefore allows for more accurate subtraction of those multiples from the data. The prior beam techniques assume that there is a single dominant multiple-generating surface 10 and the predicted multiples beams are related only to this multiple-generating surface 10 and do not contain predicted multiples from other multiple-generating surfaces. The current invention improves prior art beam techniques to incorporate predicted multiples beams from multiple-generating surfaces that were not explicitly utilized to determine to stationary peglegs.